X-TMCMC: adaptive kriging for Bayesian inverse modeling
From MaRDI portal
Publication:1737023
DOI10.1016/j.cma.2015.01.015zbMath1426.62089OpenAlexW2086716573WikidataQ59760441 ScholiaQ59760441MaRDI QIDQ1737023
Petros Koumoutsakos, Panagiotis Angelikopoulos, Costas Papadimitriou
Publication date: 26 March 2019
Published in: Computer Methods in Applied Mechanics and Engineering (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1016/j.cma.2015.01.015
Related Items (16)
Surrogate modeling of hydrodynamic forces between multiple floating bodies through a hierarchical interaction decomposition ⋮ Bayesian inversion using adaptive polynomial chaos kriging within subset simulation ⋮ Bayesian uncertainty quantification of turbulence models based on high-order adjoint ⋮ Rate-optimal refinement strategies for local approximation MCMC ⋮ On the reliability of structures equipped with a class of friction-based devices under stochastic excitation ⋮ A critical assessment of Kriging model variants for high-fidelity uncertainty quantification in dynamics of composite shells ⋮ Functional PCA and deep neural networks-based Bayesian inverse uncertainty quantification with transient experimental data ⋮ A two-stage Bayesian model updating framework based on an iterative model reduction technique using modal responses ⋮ Using Approximate Bayesian Computation by Subset Simulation for Efficient Posterior Assessment of Dynamic State-Space Model Classes ⋮ Accelerating MCMC via Kriging-based adaptive independent proposals and delayed rejection ⋮ A general two-phase Markov chain Monte Carlo approach for constrained design optimization: application to stochastic structural optimization ⋮ An enhanced substructure coupling technique for dynamic re-analyses: application to simulation-based problems ⋮ Novel nonparametric modeling of seismic attenuation and directivity relationship ⋮ Outlier detection and robust regression for correlated data ⋮ Bayesian updating with subset simulation using artificial neural networks ⋮ A physical domain-based substructuring as a framework for dynamic modeling and reanalysis of systems
Uses Software
Cites Work
- Unnamed Item
- Unnamed Item
- The pseudo-marginal approach for efficient Monte Carlo computations
- Adaptive proposal distribution for random walk Metropolis algorithm
- A survey on approaches for reliability-based optimization
- Recombination operators and selection strategies for evolutionary Markov chain Monte Carlo algorithms
- Accelerated subset simulation with neural networks for reliability analysis
- Structural reliability analysis of elastic-plastic structures using neural networks and Monte Carlo simulation
- Learning probability distributions in continuous evolutionary algorithms -- a comparative review
- Reliability-based structural optimization using neural networks and Monte Carlo simulation
- Delayed rejection in reversible jump Metropolis-Hastings
- A Stochastic Newton MCMC Method for Large-Scale Statistical Inverse Problems with Application to Seismic Inversion
- Fast Algorithms for Bayesian Uncertainty Quantification in Large-Scale Linear Inverse Problems Based on Low-Rank Partial Hessian Approximations
- Efficient Learning and Feature Selection in High-Dimensional Regression
- Accurate Approximations for Posterior Moments and Marginal Densities
- Optimal Scaling of Discrete Approximations to Langevin Diffusions
- The quickhull algorithm for convex hulls
- A sequential particle filter method for static models
- Pattern Search Algorithms for Bound Constrained Minimization
- Equation of State Calculations by Fast Computing Machines
- Monte Carlo sampling methods using Markov chains and their applications
- An adaptive Metropolis algorithm
This page was built for publication: X-TMCMC: adaptive kriging for Bayesian inverse modeling